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PD41-08 CHRONIC KIDNEY DISEASE AND END-STAGE RENAL DISEASE AFTER RADICAL OR PARTIAL NEPHRECTOMY FOR T1A RENAL CELL CARCINOMA: A POPULATION-BASED STUDY

2019· article· en· W2941321623 on OpenAlexaboutno aff
Madhur Nayan, Olli Saarela, Keith A. Lawson, Lisa J. Martin, Maria Komisarenko, Antonio Finelli

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsNephrectomyMedicineRenal cell carcinomaKidney diseasePopulationKidneyKidney cancerStage (stratigraphy)UrologyEnd stage renal diseaseDiseaseSurgeryInternal medicine

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyKidney Cancer: Localized: Surgical Therapy IV (PD41)1 Apr 2019PD41-08 CHRONIC KIDNEY DISEASE AND END-STAGE RENAL DISEASE AFTER RADICAL OR PARTIAL NEPHRECTOMY FOR T1A RENAL CELL CARCINOMA: A POPULATION-BASED STUDY Madhur Nayan*, Olli Saarela, Keith Lawson, Lisa Martin, Maria Komisarenko, and Antonio Finelli Madhur Nayan*Madhur Nayan* More articles by this author , Olli SaarelaOlli Saarela More articles by this author , Keith LawsonKeith Lawson More articles by this author , Lisa MartinLisa Martin More articles by this author , Maria KomisarenkoMaria Komisarenko More articles by this author , and Antonio FinelliAntonio Finelli More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000556552.01352.65AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Surgery remains the mainstay of treatment for localized renal cell carcinoma (RCC). However, there remains uncertainty whether partial nephrectomy is associated with a reduced risk of developing chronic kidney disease or end-stage renal disease compared to radical nephrectomy. Therefore, the objective of this study was to compare renal outcomes in patients undergoing partial or radical nephrectomy for T1a RCC. METHODS: We used administrative databases, several of which have been validated, to perform a population-based study of patients in Ontario, Canada, undergoing a partial or radical nephrectomy for T1a RCC between 1994 and 2014. We excluded patients with more than one nephrectomy or a previous history of chronic kidney disease, diabetes, or hypertension. The outcomes of interest were diagnosis of chronic kidney disease and end-stage renal disease requiring renal replacement therapy, defined as receipt of chronic dialysis or renal transplant. We used Cox proportional hazard models to evaluate the association between partial vs. radical nephrectomy and these outcomes. RESULTS: We identified 1967 patients that met inclusion criteria, of which 893 (45.5%) underwent partial nephrectomy. Patients undergoing partial nephrectomy were more likely to be younger, have a lower Charlson score, have smaller tumour sizes, and undergo surgery in more recent years. With a median follow-up in those without death of 7.6 years (interquartile range 4.3 - 12.9), 238 and 15 patients developed chronic kidney disease and end-stage renal disease, respectively. Multivariable Cox proportional hazard models found that partial nephrectomy was independently associated with a significantly reduced risk of chronic kidney disease (hazard ratio (HR) 0.16, 95% confidence interval (CI) 0.10 to 0.25). On univariate analysis, partial nephrectomy was not significantly associated with receipt of renal replacement therapy (HR 0.26, 95% CI 0.06 to 1.17). CONCLUSIONS: Our population-based study comparing partial vs. radical nephrectomy for T1a renal cell carcinoma found that partial nephrectomy was associated with significantly reduced risk of chronic kidney disease. However, the need for renal replacement therapy occurred infrequently and there was no significant association with type of surgery. Source of Funding: none Toronto, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e747-e748 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Madhur Nayan* More articles by this author Olli Saarela More articles by this author Keith Lawson More articles by this author Lisa Martin More articles by this author Maria Komisarenko More articles by this author Antonio Finelli More articles by this author Expand All Advertisement PDF downloadLoading ...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.264
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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